A Deep Reinforcement Learning Approach for Optimal Replenishment Policy in A Vendor Managed Inventory Setting For Semiconductors

• Published in: 2020 Winter Simulation Conference (WSC) pp. 1753 - 1764
• Main Authors: Tariq Afridi, Muhammad, Nieto-Isaza, Santiago, Ehm, Hans, Ponsignon, Thomas, Hamed, Abdelgafar
• Format: Conference Proceeding
• Language: English
• Published: IEEE 14.12.2020
• Subjects: Data models; Electronics industry; Key performance indicator; Reinforcement learning; Semiconductor device measurement; Semiconductor device modeling; Supply chains
• ISSN: 1558-4305
• DOI: 10.1109/WSC48552.2020.9384048

Vendor Managed Inventory (VMI) is a mainstream supply chain collaboration model. Measurement approaches defining minimum and maximum inventory levels for avoiding product shortages and over-stocking are rampant. No approach undertakes the responsibility aspect concerning inventory level status, especially in semiconductor industry which is confronted with short product life cycles, long process times, and volatile demand patterns. In this work, a root-cause enabling VMI performance measurement approach to assign responsibilities for poor performance is undertaken. Additionally, a solution methodology based on reinforcement learning is proposed for determining optimal replenishment policy in a VMI setting. Using a simulation model, different demand scenarios are generated based on real data from Infineon Technologies AG and compared on the basis of key performance indicators. Results obtained by the proposed method show improved performance than the current replenishment decisions of the company.